Parametric Model Research Articles

Incidence of constrained condylar and hinged knee implants and mid- to long-term survivorship: a register-based study from the Nordic Arthroplasty Register Association (NARA).

In complex primary total knee arthroplasty (TKA), constrained condylar knee (CCK) or rotating hinge knee (RHK) designs may be required to provide stability or address bony deficiencies. We analyzed trends in incidence of these designs in primary TKA and evaluated the mid- to long-term survivorship of CCK and RHK in 4 Nordic countries. From 2000 to 2017, 5,134 CCK and 2,515 RHK primary TKAs were identified from the NARA database. Kaplan-Meier (K-M) survival and flexible parametric survival model (FPSM) analyses were performed to estimate revision risk, expressed as hazard ratio (HR) with 95% confidence intervals (CI), with minimally stabilized (MS) TKA acting as the control group (n = 456,137). The incidence of CCK and RHK implants increased significantly in Finland, while it was moderate in Denmark, Norway, and Sweden. With revision for any reason as the endpoint the 15-year K-M cumulative revision risk for RHK was 13.6% (CI 10.4-16.7) and for CCK it was 11.3% (CI 9.1-13.5). Compared with MS TKA, the hazard ratio for revision was 2.1 (CI 1.8-2.3) for CCK and 2.5 (CI 2.1-2.8) for RHK. Periprosthetic joint infection (PJI) was the most common reason for revision, accounting for 44% of CCK and 47% of RHK cases. After excluding revisions for PJI, the hazard ratio remained high for both designs, at 1.5 (CI 1.3-1.7) for CCK and 1.6 (CI 1.3-2.0) for RHK compared with MS. The incidence of CCK and RHK increased during the study period. Both designs showed consistent 15-year revision risks of 11-14%, with no major differences between them. The higher revision risk compared with MS TKAs may reflect the complexity of the surgeries.

Ocean stratification impedes particulate transport to the plumes of Enceladus

Abstract Water-vapour plumes erupting from Enceladus’ south pole provide a window into the properties of its subsurface ocean, a prime target in the search for life. However, the extent to which plume material represents conditions at Enceladus’ depths is unclear, because of its unknown ocean stratification, which may impede the transport of matter to the ocean top. Previous studies have found conflicting stratification regimes using differing parameter choices and model physics. Here, we build a comprehensive view of Enceladus’ ocean stratification and bottom-to-top transport timescale, across plausible ranges of salinity and tidally- and librationally-induced mixing, accounting for non-linearities in the equation of state for water, geothermal heating and ice-ocean freshwater exchanges. We use theoretical models verified with global ocean numerical simulations. We show that, under a steady state assumption for the ice shell, which requires melting at the poles, there is no parameter choice permitting an unstratified ocean from top to bottom there. As a result, potential hydrothermal products take at minimum 100s of years to reach the plumes. This suggests that either timescales of several months, inferred from Cassini observations, are incorrect, perhaps biased by alternative particulate transport mechanisms, or that Enceladus’ ice shell is not in a quasi-equilibrated state.

An Improved Parametric 3D Geological Modeling Framework for Seismic Structure Identification Using Deep Learning in Complex Geological Settings

Identifying geological structures from seismic images is critical in understanding the Earth's evolutionary history, assessing geological disasters, and predicting resource distribution. Researchers have used synthetic seismic data with structure labels generated by numerical simulation to train deep learning models to avoid the extensive effort and resources required to manually label seismic data. However, most existing studies are task-oriented and often simplify or even ignore some common and important geological features during the simulation process, which limits the performance of the trained model on real datasets. To further improve the generality and flexibility of deep learning models for structure identification on datasets with complex structural features, we propose an improved parametric 3D structure modeling framework for numerical simulation of synthetic seismic data that include dip, fold, unconformity, channel, cave, salt body, and composite fault structure. Moreover, we revise some simulation solutions to make the simulated geological structures better resemble the real ones. To the best of our knowledge, our synthetic seismic dataset has the richest structural features to date. The results of comparative experiments on multiple synthetic and field datasets reveal that a deep learning model trained on datasets with limited geological features is prone to learn biased representations, which causes the model to misclassify unseen geological structures with similar seismic responses to the target structure. On the contrary, the same deep learning model trained on the proposed dataset provides significantly reduced false predictions of structures with similar seismic responses and improves the identification accuracy of datasets with complex geological features. Although this study only takes fault and channel identification as case studies, the proposed synthetic seismic dataset can be easily adapted to identify other seismic structures.

Spatiotemporal Dynamics and Driving Mechanism of Aboveground Biomass Across Three Alpine Grasslands in Central Asia over the Past 20 Years Using Three Algorithms

Aboveground biomass (AGB) is a sensitive indicator of grassland resource quality and ecological degradation. However, accurately estimating AGB at large scales to reveal long-term trends remains challenging. Here, single-factor parametric models, multi-factor parametric models, and multi-factor non-parametric models (Random Forest) were developed for three grassland types (alpine meadow, alpine grassland, and swampy meadow) in the Bayanbuluk Grassland using MODIS satellite data and environmental factors, including climate and topography. A 10-fold cross-validation method was employed to assess the accuracy and stability of these models, and an AGB remote sensing inversion model was established to estimate the AGB of the Bayanbuluk Grassland from 2005 to 2024. Moreover, the BEAST mutation test, Theil–Sen median trend analysis, and Mann–Kendall test were used to analyse the temporal trends of AGB, identify the years of mutation points, and explore the changes in AGB across the entire study period (2005–2024) and at 5-year intervals, considering the influence of climatic factors. The results indicated that the machine learning (RF) model outperformed both multi-factor parametric and single-factor parametric models, with specific improvements in R2 and RMSE across all grassland types. For instance, the RF model achieved an R2 of 0.802 in alpine grasslands, outperforming the multi-factor parametric model with an R2 of 0.531. The overall spatial distribution of AGB exhibited heterogeneity, with a gradual increase from northwest to southeast over the study period. Interannual AGB changes fluctuated significantly, with an overall increasing trend. Notably, from 2015 to 2019, 78% of the Bayanbuluk Grassland area showed a nonsignificant increase in AGB. Specifically, 46.7% of the alpine meadow AGB, 23% of the alpine grassland AGB, and 8.3% of the swampy meadow AGB showed non-significant increases. Further, temperature was found to be the dominant driver of AGB, with a stronger effect on alpine meadows and alpine grasslands than on swampy meadows. This is likely due to the relatively constant moisture levels in the swampy meadows, where precipitation plays a more prominent role. This study provides a comprehensive assessment of AGB trends, including both spatial and temporal analyses, which will inform future grassland resource management.

Performance-informed urban design: training a generalized surrogate model for predicting building energy demand across residential morphologies in Singapore

Building performance simulations (BPS) are essential for managing rising energy demands but are computationally intensive, limiting their use in early-stage design. Surrogate models (SMs) offer fast approximations using machine learning but lack generalizability for changing building morphologies. This study introduces three morphology-based feature extraction methods – Zernike image moments, polar raycasting, and principal component analysis – to train a generalizable SM for predicting annual energy use intensity (EUI) of 2.5D residential buildings in Singapore. The best SM achieved a 9.2% RMSPE, with features yielding low error and avoiding overfitting. This framework enables efficient design space exploration, aiding architects and urban designers in optimizing masterplans for energy efficiency. In the final section, we demonstrate a design space exploration (DSE) framework with our generalizable SM using a novel parametric model, showcasing its speed, adaptability and convenience over conventional optimization workflows. Highlights Novel approach explores building morphology's impact to annual EUI . Using Zernike moments, ray casting and PCA to extract features from any 2.5D buildings. 4000 tuned SMs trained from hyperparameter optimisation. Most robust SM achieved 9.2% RMSPE when tested with 800 samples. SM's generalizability is proven by applying it to a design model with previously unseen parameters.

Development of a Microsimulation Model to Project the Future Prevalence of Childhood Cancer in Ontario, Canada.

Estimates of the future prevalence of childhood cancer are informative for health system planning but are underutilized. We describe the development of a pediatric oncology microsimulation model for prevalence (POSIM-Prev) and illustrate its application to produce projections of incidence, survival, and limited-duration prevalence of childhood cancer in Ontario, Canada, until 2040. POSIM-Prev is a population-based, open-cohort, discrete-time microsimulation model. The model population was updated annually from 1970 to 2040 to account for births, deaths, net migration, and incident cases of childhood cancer. Prevalent individuals were followed until death, emigration, or the last year of simulation. Median population-based outcomes with 95% credible intervals (CrIs) were generated using Monte Carlo simulation. The methodology to derive model inputs included generalized additive modeling of cancer incidence, parametric survival modeling, and stochastic population forecasting. Individual-level data from provincial cancer registries for years 1970 to 2019 informed cancer-related model inputs and internal validation. The number of children (aged 0-14 y) diagnosed with cancer in Ontario is projected to rise from 414 (95% CrI: 353-486) in 2020 to 561 (95% CrI: 481-653) in 2039. The 5-y overall survival rate for 2030-2034 is estimated to reach 90% (95% CrI: 88%-92%). By 2040, 24,088 (95% CrI: 22,764-25,648) individuals with a history of childhood cancer (diagnosed in Ontario or elsewhere) are projected to reside in the province. The model accurately reproduced historical trends in incidence, survival, and prevalence when validated. The rising incidence and prevalence of childhood cancer will create increased demand for both acute cancer care and long-term follow-up services in Ontario. The POSIM-Prev model can be used to support long-range health system planning and future health technology assessments in jurisdictions that have access to similar model inputs. This article describes the development of a population-based, discrete-time microsimulation model that can simulate incident and prevalent cases of childhood cancer in Ontario, Canada, until 2040.Use of an open cohort framework allowed for estimation of the potential impact of net migration on childhood cancer prevalence.In addition to supporting long-term health system planning, this model can be used in future health technology assessments, by providing a demographic profile of incident and prevalent cases for model conceptualization and budget impact purposes.This modeling framework is adaptable to other jurisdictions and disease areas where individual-level data for incidence and survival are available.

STRUCTURAL-PARAMETRIC SHAPING OF SOIL TILLAGE TOOLS

One of the important directions in the modern development of the scientific school of applied geometry at the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" is the methodology of structural and parametric shaping of various technical objects. Significant theoretical and practical achievements have been made in this field in recent years. In particular, this applies to implementations in the aviation industry, general mechanical engineering, and other sectors of the national economy. The current difficult period in Ukraine is related to the military actions on its territory. In such conditions, agriculture has become a significant component of our country's economy. Therefore, further improvement of this sector represents a pressing scientific and applied problem. One of the ways to successfully solve the outlined problems is, in particular, the improvement of the used tillage tools. In this regard, defining the optimal shape and dimensions of these technical objects is considered progressive. These factors not only significantly improve the quality of soil cultivation but also contribute to the preservation of its fertility, reduction of energy consumption in the corresponding technological processes, and so on. The application of computer-based structural and parametric geometric modeling enables productive and flexible implementation of multi-faceted analysis of a large number of design options during the development of industrial products. This approach ensures comprehensive optimization, including various tillage implements. An important aspect of this process is the use of integrated generalized geometric models that encompass a wide range of technical objects. This concept significantly enhances the efficiency of existing computer information technologies. This article reviews some fundamental results obtained regarding the structural and parametric shaping of certain agricultural machinery products. For example, this includes tillage disks, chisel plows, and moldboard plows. The publication also presents proposed new methods and techniques of geometric modeling, outlines their development directions, and identifies the prospects for conducting further related scientific research.

Monetization and the Fiscal Multiplier

ABSTRACTWe investigate the size of Italian fiscal multipliers over the period 1872–2006. By instrumenting total expenditure with defence expenditure, we measure the multiplier under different business and monetary policy regimes, also employing a time‐varying parameters model to encompass structural changes. We explore the nexus between the multiplier and monetization using the treasury monetary base, a policy instrument used in Italy during the period, as a novel measure of monetization. Results suggest that when monetization and economic slackness occur jointly, the fiscal multiplier tends to be larger. Monetization does not significantly affect the multiplier during expansions.

The clinical implications and cost-effectiveness of the provision of medical in addition to surgical catheter insertion for peritoneal dialysis in people with kidney failure.

High-quality and timely peritoneal access is essential for effective peritoneal dialysis (PD). Existing comparisons of medical compared to surgical catheter insertion have focused on the incidence of catheter events, but the cost-effectiveness of providing medical in addition to surgical catheter insertion in a dual pathway, compared to providing surgical insertion alone has not been evaluated. Data from the UK Catheter study, exploring how patient, service and insertion technique factors interact was used to estimate the comparative rates of catheter events between medical and surgical catheter insertion. A cost-effectiveness model estimates the health benefits and costs of providing medical in addition to surgical catheter insertion, compared to surgical insertion alone. Parametric modelling estimated time to catheter events, haemodialysis and transplantation to populate the model. Data on 769 first catheter insertions informs the model (325 medical and 444 surgical). Fewer catheter events were observed with medical insertion. The dual insertion pathway (69% medical, 31% surgical) was therefore associated with lower lifetime catheter events (3.18 vs. 3.34) and longer time on PD (3.07 vs. 3.00 years) than a purely surgical insertion pathway. The lifetime mean differences in quality-adjusted life years (7.12 vs. 7.00) and near identical costs (£226,549 vs. £226,764) meant dual insertion pathway was likely to be cost-effective, a finding robust to a series of sensitivity analyses. Offering medical in addition to surgical catheter insertion techniques has the potential to improve clinical outcomes and is likely to be highly cost-effective compared to surgical insertion alone.

A Multivariate Approach for Modeling Spatio‐Temporal Agrometeorological Variables

ABSTRACTOne of the main issues facing agrometeorological studies involves measuring and modeling the evolution of different environmental variables over time; this often requires a dense monitoring network. Spatio‐temporal geostatistics has the potential to provide techniques and tools to estimate the spatio‐temporal multiple covariance function and define an appropriate multivariate correlation function capable of reliable predictions. This paper presents a spatio‐temporal multivariate geostatistical modeling approach based on the joint diagonalization of the empirical covariance matrix evaluated at different spatio‐temporal lags. The possibility to consider a reduced number of uncorrelated variables (lower than the number of observed variables) and separately model the spatio‐temporal evolution of these uncorrelated components represents a substantial simplification for multivariate modeling. A space–time linear coregionalization model (ST‐LCM) with appropriate parametric models for the latent components was fitted to the matrix‐valued covariance function estimated for five relevant agrometeorological variables, including evapotranspiration, minimum and maximum humidity, maximum temperature, and precipitation. The analyses highlight how to identify space–time components and choose the corresponding model by evaluating some characteristics of these components, such as symmetry, separability, and type of non‐separability. The predictive results of this multivariate study will be of interest for agriculture, in particular for addressing drought emergencies.

Effectiveness of PEER Intervention on Older Adults’ Physical Activity Time Series Using Smoothing Spline ANOVA

Falls are a major cause of injury among older adults. The Physio-fEedback Exercise pRogram (PEER) combines physio-feedback, cognitive reframing, and guided exercises to reduce fall risk. However, its impact on physical activity (PA) over time is underexplored. Functional time-series analysis offers insight into behavior patterns and sustainability. This preliminary study assessed PEER’s effectiveness in improving PA levels immediately and over time. A total of 64 community-dwelling older adults were cluster-randomized into PEER (N=33) or control groups (N=31). Participants wore Fitbit trackers, generating time-series data on activity. The PEER group completed an 8-week program, while the control group received CDC fall prevention pamphlets. PA data were analyzed using smoothing spline analysis of variance (SSANOVA), chosen for its flexibility in modeling complex, non-linear relationships in time-series data and its ability to handle skewed distributions and repeated measures. Unlike traditional parametric models, SSANOVA decomposes temporal trends into interpretable components, capturing both smooth trends and abrupt changes, such as those occurring on group workout days. This capability ensures robust and nuanced analysis of intervention effects. Results showed PEER participants significantly increased evenly and had very active minutes and reduced sedentary behavior during the intervention. No significant effect was found for light active minutes. Specifically, during the intervention period, PEER participants engaged in an average of 6.7% fewer sedentary minutes per day, 13.8% additional fairly active minutes per day, and 2.8% additional very active minutes per day compared to the control group. While the reduction in sedentary minutes and increase in fairly active minutes were not statistically significant, the increase in very active minutes was significant. However, our functional time-series analysis revealed these improvements diminished over the 15-week follow-up, indicating challenges in maintaining PA. In conclusion, PEER boosts PA and reduces sedentary behavior short-term, but strategies are needed to sustain these benefits. In conclusion, PEER boosts PA and reduces sedentary behavior short-term, but strategies are needed to sustain these benefits. Public health policies should emphasize technology-driven fall risk assessments, community-based prevention programs, and initiatives that promote physical activity, home safety, and chronic condition management.

Reconstruction of Rock Composed of Multiple Irregular Minerals and Microcracks Using the Modified Quartet Structure Generation Set Method

ABSTRACTIn this study, a modified reconstruction method for rock that considers the irregularity of minerals and microcracks was proposed. First, the quartet structure generation set (QSGS) method was modified by establishing two different growth modes of initial growth cores to generate the minerals and microcracks. Then, three‐dimensional (3D) digital rock modeling software was developed based on the modified method, achieving efficient and parametric rock modeling using real rock characteristic parameters. Next, taking granite as an example, a series of digital rock models were reconstructed based on the real characteristic parameters obtained by x‐ray diffraction (XRD) and computed tomography (CT) tests. Finally, the accuracy of the modified method was verified by comparing the fractal dimensions of minerals and microcracks of digital rock models and granite sample. The results show that 3D digital rock modeling software can generate digital rock models with different mineral contents and porosities and that the generation time of the model decreases exponentially as the grid size increases. Meanwhile, the fractal dimensions of minerals and microcracks in the digital rock models are similar to those in the granite sample with acceptable errors of ± 5.0%. In summary, the modified method can reconstruct rock accurately and effectively by considering the irregularity of minerals and microcracks.

FireVoxel: Interactive Software for Multi-Modality Analysis of Dynamic Medical Images.

This article provides an overview of the FireVoxel software for quantitative analysis of medical images and its applications in the field. We describe FireVoxel's user interface, multi-layer design, dynamic parametric models, and several turn-key workflows. Additionally, we discuss its application in recent imaging projects. We outline basic image analysis tools such as segmentation, non-uniformity correction, and coregistration through a pictorial overview, with a focus on deformable coregistration and motion correction. Several example workflows and image-based dynamic modeling are also highlighted. Furthermore, we analyze peer-reviewed studies that utilized FireVoxel for image processing, categorizing published papers based on body structures/organs, image processing methods, and imaging modalities. For comparison, we searched the Ovid MEDLINE database to assess the general use of medical image analysis software. FireVoxel is used by over 3000 users worldwide, with 528 articles, including 413 in English, published in the past 15years. MRI is the most commonly used imaging modality (78.2%), followed by CT (14.5%) and PET (7.3%). The most frequently used methods are dynamic modeling, segmentation, texture analysis, and coregistration. FireVoxel is commonly used in abdominal and genitourinary imaging studies, where it appears to fill a niche due to the lack of alternative software. The search of the Ovid MEDLINE suggests that quantitative medical imaging studies, on the other hand, focus on the brain and cardiovascular system. FireVoxel offers an effective set of quantitative tools, particularly for abdominal and genitourinary imaging, likely due to its ability to manage patient motion and correct for MR artifacts. The software is especially valuable for processing dynamic studies. The steady increase in publications utilizing FireVoxel reflects growing interest in this software and its relevance for image-based research.

Semi-Parametric Item Response Theory With O'Sullivan Splines for Item Responses and Response Time.

Response time (RT) has been an essential resource for supplementing the estimation accuracy of latent traits and item parameters in educational testing. Most item response theory (IRT) approaches are based on parametric RT models. However, since test takers may alter their behaviors during a test due to motivation or strategy shifts, fatigue, or other causes, parametric IRT models are unlikely to capture such subtle and nonlinear information. In this work, we propose a novel semi-parametric IRT model with O'Sullivan splines to accommodate the flexible mean RT shapes and explore the underlying nonlinear relationships between latent traits and RT. A simulation study was conducted to demonstrate the substantial improvement in parameter estimation achieved by the new model, as well as the detriment of using parametric models in terms of biases and measurement errors. Using this model, a dataset of mathematics test scores and RT from the Programme for International Student Assessment was analyzed to demonstrate the evident nonlinearity and to compare the proposed model with existing models in terms of model fitting. The findings presented in this study indicate the promising nature of the new approach, suggesting its potential as an additional psychometric tool to enhance test reliability and reduce measurement errors.

Prediction of building-scale solar energy potential in urban environment based on parametric modelling and machine learning algorithms

Prediction of building-scale solar energy potential in urban environment based on parametric modelling and machine learning algorithms

A parameterized model of center-symmetric space deployable arrays inspired by Miura and five-crease origami

Thick-panel origami has gained significant attention for its potential applications in the design of space deployable arrays. However, existing origami structures often fail to meet the stringent constraints imposed by various spacecraft in terms of connectivity and space efficiency, limiting the achievement of optimal folding ratios and compact transportation. To address these challenges, we propose a parameterized geometric model and design method for center-symmetric thick-panel space deployable arrays for varying scales and functions, integrating five-crease vertices with Miura origami. A planar four-bar linkage mechanism is applied to coordinate the motion between the five-crease vertices and Miura origami, ensuring one-degree-of-freedom (one-DOF) motion. The proposed model is validated through a case study on a space exposure experiment platform, with a scaled-down prototype fabricated for testing. The resulting space deployable arrays can be compactly placed within a rectangular prism, offering central symmetry, one-DOF motion, high volume efficiency and folding ratio, and enhanced design flexibility and adaptability.

Analytical and experimental study on acoustic-vibration characteristics of double-helical planetary gear transmission systems with multi-field coupling effect

The acoustic vibration characteristics play a crucial role in determining the performance of aero double-helical planetary gear transmission systems. Considering the coupling effect among temperature, fluid, and structure in double-helical planetary gear transmission systems, an improved mesh stiffness model is constructed in this paper, which also considers the fractal tooth, tooth friction, thermal expansion, and load-sharing of “oil film-asperity”. Based on the study of mesh stiffness of fractal tooth, a centralized parametric dynamic model is established, considering the influence of friction excitations, transmission error, and tooth clearance. The bearing stiffness and the obtained dynamic mesh forces are then used as excitation sources to calculate the vibration of the gear system with its housing. Furthermore, the housing vibration displacement in the frequency domain is used as the boundary condition of the acoustic boundary element model to calculate the housing surface sound pressure and the field point sound pressure of the gearbox. Finally, experiments on vibration and acoustics are conducted to verify the correctness of the theoretical investigation. The results show that the mesh stiffness of fractal double-helical gear pairs with multi-field coupling effect is closer to the simulation value of the finite element method (FEM), the tested vibration is close to the value of the simulation, and the tested sound pressure level is close to the value obtained by the boundary element method (BEM), which indicates the validity of the proposed methods.

Measuring healthy life expectancy and determinants of poor perceived health: A population-based study among a subset of rare and common cancer survivors.

As the survival proportions for rare cancers are on average worse than for common cancers, assessing the expected remaining life years in good health becomes highly relevant. This study aimed to estimate the healthy life expectancy (HLE) of a subset of rare and common cancer survivors, and to assess the determinants of poor perceived health in rare cancer survivors. To calculate HLE, survival data from the population-based Netherlands Cancer Registry of survivors of a rare cancer (i.e., ovarian cancer, thyroid cancer, Hodgkin lymphoma, non-Hodgkin lymphoma) (n=21,376) and a common cancer (i.e., colorectal cancer (CRC)) (n=76,949) were combined with quality of life (QoL) data from the PROFILES registry on a random sample of the rare (n=1025) and common cancer (n=2400) survivors. A flexible parametric relative survival model was used to estimate life expectancy (LE) and years of life lost, and multivariate logistic regression was applied to determine factors related to reported poor perceived health. Patients previously diagnosed with a rare cancer had an average LE of 8-36 years and were expected to spend ≥67 % of their remaining life in good health. CRC survivors had an average LE of 10 years with approximately 65 % of their remaining life expected to spend in good health. For all cancer types, those aged ≥65 years or with stage IV had the lowest HLE. Low socioeconomic status, advanced stage, and having received radiotherapy only were important predictors of poor perceived health among rare cancer survivors. HLE can provide meaningful perspective for patients and practitioners for all cancer types, including rare cancers. Yet, data on QoL for rare cancers should be routinely collected, as such will serve as an indicator for monitoring and improving cancer care, and for enabling HLE measurements in cancer survivors.

Measuring the effects of motion corruption in fetal fMRI.

Irregular and unpredictable fetal movement is the most common cause of artifacts in in utero functional magnetic resonance imaging (fMRI), affecting analysis and limiting our understanding of early functional brain development. The accurate detection of corrupted functional connectivity (FC) resulting from motion artifacts or preprocessing, instead of neural activity, is a prerequisite for reliable and valid analysis of FC and early brain development. Approaches to address this problem in adult data are of limited utility in fetal fMRI. In this study, we evaluate a novel technique for robust computational assessment of motion artifacts, and the quantitative comparison of regression models for artifact removal in fetal FC analysis. It exploits the association between dynamic FC and non-stationarity of fetal movement, to detect residual noise. To validate our motion artifact detection technique in detail, we used a parametric generative model for neural events and fMRI blood oxygenation level-dependent (BOLD) signal. We conducted a systematic evaluation of 11 commonly used regression models in a sample of 70 fetuses with gestational age of 19-39 weeks. Results demonstrate that the proposed method has better accuracy in identifying corrupted FC compared to methods designed for adults. The technique, suggests that censoring, global signal regression and anatomical component-based regression models are the most effective models for compensating motion. The benchmarking technique, and the generative model for realistic fetal fMRI BOLD enables investigators conducting in utero fMRI analysis to effectively quantify the impact of fetal motion and evaluate alternative regression strategies for mitigating this impact. The code is publicly available at: https://github.com/cirmuw/fetalfMRIproc.

Advanced Soil Moisture Predictive Methodology in the Maize Cultivation Region

Soil moisture is a critical determinant of the maize crop health and productivity. With over 60% of India's maize cultivation concentrated in South Indian states, accurately forecasting soil moisture is essential for optimizing irrigation and enhancing agricultural output. This study introduces an Improved Hybrid Machine Learning (IHML) model that integrates and optimizes Machine Learning (ML) models to deliver superior predictive performance. By leveraging data from key maize-growing districts in South India, the IHML model demonstrates enhanced convergence rates and accuracy compared to traditional ML approaches. The research framework is grounded in comprehensive correlation evaluations, which inform parameter selection and model architecture. Extensive comparisons reveal that the IHML model significantly outperforms individual ML models in forecasting soil moisture with higher precision. These findings highlight the potential of IHML models to advance smart farming practices and enable precise irrigation management, paving the way for improved crop yield and sustainable agriculture.